Riesling acetal is a precursor to 1,1,6-trimethyl-1,2-dihydronaphthalene (TDN) in wine

Background and Aims:  Knowledge of the formation and fate of aroma compounds is important to an understanding of the processes that affect wine composition and flavour. This study aimed to investigate the competitive formation and degradation of 1,1,6-trimethyl-1,2-dihydronaphthalene (TDN) and 2,2,6,8-tetramethyl-7,11-dioxatricyclo[6.2.1.0^1,6]undec-4-ene (Riesling acetal) under wine storage conditions.
Methods and Results:  1,1,5-trimethyl-3,6-dihydroxy-9,9-ethylenedioxymegastigm-4-ene was synthesised in the laboratory by using standard organic chemistry transformation methodology and was subsequently used to prepare both Riesling acetal and TDN. At 45°C in model wine, approximately 40% of Riesling acetal was converted to TDN after 60 days at pH 3.2, whereas 80% was converted after 60 days at pH 3.0, as established by GC/MS analysis. Under more vigorous conditions, this conversion was complete and the TDN thus formed was completely stable.
Conclusion:  Riesling acetal is a precursor to TDN at wine pH, rather than a competing end-product formed by the acid-catalysed hydrolysis of precursors common to both compounds. TDN is chemically stable to wine acid.
Significance of the Study:  The findings provide a clearer understanding of the factors affecting the concentration of TDN (kerosene, bottle-age aroma) in older wines and thereby have the potential to indicate strategies for modulating the formation of TDN.     

A Review of: “THEORY OF HIERARCHICAL,MULTILEVEL SYSTEMS”, by M. D. Mesarovic,D. Macko and Y. Takahara. Academic Press,New York, 1970. 294 pp.

A number of “normalized” measures of entropy have been obtained to measure the “intrinsic” uncertainty of a probability distribution. Their graphs have been drawn and it has been shown that the normalized measures of a given probability distribution are much closer to one another than the corresponding absolute measures of entropy.     

An application of H∞ design to model-following

The present work shows the application of H_∞ optimal control to the imperfect model-following problems where exact model-following fails to be implemented. The significance of this work lies in the improvement of the ill-conditioning problem in the H_∞ algorithm for the strictly proper plant. This is achieved by perturbing the plant in a suitable way. The optimal results can be recovered asymptotically under certain conditions for the scalar case. Such an approach provides a way to solve the problem when the existing H_∞ algorithm fails. This paper also highlights the degree constraints of choosing an ideal model. The model-following controller is illustrated by practical examples and compared with H₂ results.     

THERMAL INACTIVATION OF SALMONELLA ENTERITIDIS BY BOILING AND FRYING EGG METHODS

Salmonella enteritidis was cultivated in egg yolks and submitted to boiling and frying methods commonly used in domestic conditions in the south of Brazil. The results have demonstrated a rapid growth of the microorganism reaching counts of 9.0 log₁₀at 168 h. When contaminated shell eggs were placed in water and slowly heated, a population of approximately 7.0 log₁₀was completely inactivated after boiling for 1 min. However, in eggs placed directly in boiling water, the reduction was only 1.35 log₁₀after 1 min, and the entire population was eliminated after 3 min. A reduction of approximately 2.78 log₁₀was observed after frying for 1.5, 2.0 and 2.5 min. Even though the solidification of yolk has been pointed out as an indicator of S. enteritidis elimination in egg yolks, attention should be given to frying procedures, as considerable high levels of this microorganism could be recovered even from solidified yolks.     

On the identification of input-output-noise models

The problem of consistently estimating all parameters of an input-output noise model is considered. It is shown that the parameters of the input-output (deterministic) port of the model may first be estimated consistently when a pure auto-rogressive structure of that part is considered. Subsequently, if a consistent estimation procedure of the noise model is employed, a consistent estimate of the parameters of the complete input-output-noise model is shown to be obtainable.     

DEMON-BASED ASSOCIATIVE MEMORIES

Associative memories are of two fundamental types, those that store representations of prototypical patterns (auto-associative memories)and those that store associations between pairs of arbitrary patterns (hetero-associative memories)Four network models of the latter type, each employing a single layer of linear threshold units are presented. Two of these models maintain fixed arrangements of their components. The other two are dynamically self-organizing. They employ feedback about performance to guide changes in the organization of their components. These models are evaluated in terms of storage capacity, error-tolerance, and storage space efficiency. One form of dynamic memory has the highest storage capacity of any known network model of associative memory. A discussion of models by Anderson and Hopfield and some implications of static and dynamic architectures conclude the paper.